Systems and methods for guided de-noising for computed tomography

ABSTRACT

A method includes obtaining spectral computed tomography (CT) information via an acquisition unit having an X-ray source and a CT detector. The method also includes, generating, with one or more processing units, using at least one image transform, a first basis image and a second basis image using the spectral CT information. Further, the method includes performing, with the one or more processing units, guided processing on the second basis image using the first basis image as a guide to provide a modified second basis image. Also, the method includes performing at least one inverse image transform using the first basis image and the modified second basis image to generate at least one modified image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.14/566,874, which was filed on Dec. 11, 2014 and is incorporated hereinby reference in its entirety.

BACKGROUND OF THE INVENTION

The subject matter disclosed herein relates generally to systems andmethods for computed tomography (CT) imaging.

In CT imaging, an X-ray source may be rotated around an object to obtainimaging information. X-rays from the source attenuated by the object maybe collected or detected by a detector and used to reconstruct an image.Due to variations in attenuation as a function of energy level amongmaterials, spectral CT imaging provides the ability to distinguishdifferent materials even if the materials have similar attenuation forconventional single-energy CT scans at a given energy. Spectral CTimaging may be used to provide synthetic monochromatic images usinglinear combinations of material decomposed (MD) images. However, the rawMD images (i.e., water and iodine), as the direct results of filteredback-projection, may suffer significant amounts of negatively correlatednoise resulting from projection-space material decomposition. Subsequentsteps for noise reduction may thus be required. At some energies, one orthe other of the MD images may tend to dominate, while at other energiesthe negatively correlated noise may essentially be cancelled. When oneof the MD images tends to dominate, additional noise reduction may berequired to keep noise at acceptable levels in synthesized monochromaticimages while preserving spatial resolution and image quality (IQ).

Conventional noise reduction approaches may not achieve consistentlylower noise in synthetic monochromatic images across all photon energylevels, as compared to raw MD images. For example, if conventionallyde-noised MD images are combined, the noise of the resultingmonochromatic image may contain more noise than the combination of rawMD images for a range of photon energies. Raw and de-noised images maybe blended when producing monochromatic images, in order to select whichMD images (raw or de-noised) should be combined to produce lower noiseacross an entire energy range. However, such blending results inunnecessarily large memory usage and storage requirements, as theinformation to de-noise images on-the-fly is stored along with the rawMD image information. Further, conventional de-noising approaches maynot achieve sufficient noise reduction at low dosage levels, and/or mayintroduce artifacts that compromise image texture and spatialresolution.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, a method is provided that includes obtaining spectralcomputed tomography (CT) information via an acquisition unit having anX-ray source and a CT detector. The method also includes, generating,with one or more processing units, using at least one image transform, afirst basis image and a second basis image using the spectral CTinformation. Further, the method includes performing, with the one ormore processing units, guided processing (e.g., de-noising) on thesecond basis image using the first basis image as a guide to provide amodified second basis image. Also, the method includes performing atleast one inverse image transform using the first basis image and themodified second basis image to generate at least one modified image.

In another embodiment, an imaging system is provided that includes acomputed tomography (CT) acquisition unit and a processing unit. The CTacquisition unit includes an X-ray source and a CT detector configuredto collect spectral CT information of an object to be imaged. The X-raysource and the CT detector are configured to be rotated about the objectto be imaged and to collect a series of projections of the object atplural energy levels as the X-ray source and CT detector rotate aboutthe object to be imaged. The processing unit includes at least oneprocessor operably coupled to the CT acquisition unit. The processingunit is configured to control the CT acquisition unit to collect thespectral CT information of the object to be imaged. The processing unitis also configured to generate, using at least one image transform, afirst basis image and a second basis image using the spectral CTinformation. Further, the processing unit is configured to performguided processing on the second basis image using the first basis imageas a guide to provide a modified second basis image. Further, theprocessing unit is also configured to perform at least one inversetransform using the first basis image and the modified second basisimage to generate at least one modified image.

In another embodiment, a tangible and non-transitory computer readablemedium is provided that includes one or more computer software modulesconfigured to direct one or more processors to obtain spectral computedtomography (CT) information via an acquisition unit comprising an X-raysource and a CT detector; generate, using at least one image transform,a first basis image and a second basis image using the spectral CTinformation; perform guided processing on the second basis image usingthe first basis image as a guide to provide a modified second basisimage; and perform at least one inverse image transform using the firstbasis image and the modified second basis image to generate at least onemodified image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic process flow for guided de-noising inaccordance with various embodiments.

FIG. 2 illustrates results of an example guided de-noising in accordancewith various embodiments.

FIG. 3 provides a comparison of results between conventional de-noisingand guided de-noising in accordance with various embodiments.

FIG. 4 illustrates a graph comparing noise measured in monochromaticimages across different photon energies that have been synthesized fromMD images.

FIG. 5 illustrates a schematic process flow for guided de-noising inaccordance with various embodiments.

FIG. 6 is a flowchart of a method in accordance with variousembodiments.

FIG. 7 is a schematic block diagram illustrating an imaging system inaccordance with various embodiments.

FIG. 8 is a schematic block diagram of an imaging system in accordancewith various embodiments.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description of certain embodiments will be betterunderstood when read in conjunction with the appended drawings. To theextent that the figures illustrate diagrams of the functional blocks ofvarious embodiments, the functional blocks are not necessarilyindicative of the division between hardware circuitry. For example, oneor more of the functional blocks (e.g., processors or memories) may beimplemented in a single piece of hardware (e.g., a general purposesignal processor or a block of random access memory, hard disk, or thelike) or multiple pieces of hardware. Similarly, the programs may bestand alone programs, may be incorporated as subroutines in an operatingsystem, may be functions in an installed software package, and the like.It should be understood that the various embodiments are not limited tothe arrangements and instrumentality shown in the drawings.

As used herein, the terms “system,” “unit,” or “module” may include ahardware and/or software system that operates to perform one or morefunctions. For example, a module, unit, or system may include a computerprocessor, controller, or other logic-based device that performsoperations based on instructions stored on a tangible and non-transitorycomputer readable storage medium, such as a computer memory.Alternatively, a module, unit, or system may include a hard-wired devicethat performs operations based on hard-wired logic of the device.Various modules or units shown in the attached figures may represent thehardware that operates based on software or hardwired instructions, thesoftware that directs hardware to perform the operations, or acombination thereof.

“Systems,” “units,” or “modules” may include or represent hardware andassociated instructions (e.g., software stored on a tangible andnon-transitory computer readable storage medium, such as a computer harddrive, ROM, RAM, or the like) that perform one or more operationsdescribed herein. The hardware may include electronic circuits thatinclude and/or are connected to one or more logic-based devices, such asmicroprocessors, processors, controllers, or the like. These devices maybe off-the-shelf devices that are appropriately programmed or instructedto perform operations described herein from the instructions describedabove. Additionally or alternatively, one or more of these devices maybe hard-wired with logic circuits to perform these operations.

As used herein, an element or step recited in the singular and precededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” are not intended to beinterpreted as excluding the existence of additional embodiments thatalso incorporate the recited features. Moreover, unless explicitlystated to the contrary, embodiments “comprising” or “having” an elementor a plurality of elements having a particular property may includeadditional elements not having that property.

Various embodiments provide systems and methods for reduction of noisein material decomposition images and/or images (e.g., syntheticmonochromatic images) formed or generated using material decompositionimages. Various embodiments utilize noise reduction algorithms ortechniques that reduce correlated noise in spectral (e.g., dual energy)CT images. Some embodiments utilize performance of de-noising in atransformed domain where correlated noise is approximatelyde-correlated. For example, in some embodiments, a linear transformationis first conducted to convert raw MD images to an alternative domain(e.g., two MD images may be combined to provide two transformed images),where the transform is configured to provide low correlated noise (e.g.,approximately minimal) in one of the transformed images but a relativelylarge amount of noise in the other transformed image. Then, the imagewith the lower amount of correlated noise (e.g., a first transformedimage or a first basis image) may be used as a guide to de-noise theimage with larger amount of correlated noise (e.g., a second transformedimage or second basis image). Next, the first transformed image (theimage with the lower amount of correlated noise) and the de-noisedsecond transformed image may be transformed back to MD domain to obtainde-noised MD images. Further, one or more synthetic monochromatic imagesmay be generated using a linear combination of the de-noised MD images.In some embodiments, the transform used to provide the transformedimages (or basis images for the de-noising of a second basis imageguided by a first basis image) from the raw material images may be thesame or similar to the transform used to provide a monochromatic imagefrom the de-noised material images, while in other embodiments thetransforms may be different.

In various embodiments, noise may be reduced utilizing a principle ofnegative noise correlation that may exist in MD images after a materialdecomposition process, for example, by applying a linear transform toprovide basis images. For example, from raw material decompositionimages, a 70 keV equivalent monochromatic image may be created by addingthe material decomposition images to provide a first transformed orfirst basis image, while a second transformed image or second basisimage may be formed from a subtraction of the MD images. In the additionimage, the correlated noise cancels out, leading to a low-noise highsignal image in the monochromatic domain. In the subtraction image, thecorrelated noise is doubled and the image corresponds to a differentialsignal. The subtraction image may then be relatively easier to de-noisethan either of the raw MD images. For example, the addition image may beused as a guide to determine features such as organ edges that should bepreserved in the subtraction image, or features across which filteringshould not take place. After the subtraction image is de-noised, aninverse linear transformation using the addition image and the de-noisedsubtraction image may be applied to convert the images back to the MDdomain, which results in de-noised MD images. In various embodiments,one or more additional de-noising steps (or other processing steps suchas edge enhancement) may be performed for further image qualityenhancement.

It may be noted that other images and/or transforms may be employed withguided de-noising in various embodiments. For example, transforms otherthan addition or subtraction may be used to provide the transformed orbasis images. As another example, in dual-energy CT, generally a firstprojection obtained at a higher energy will have a lower noise levelthan a second projection obtained at a lower energy. Accordingly, invarious embodiments, high-energy projection data may be used to de-noiselow-energy projection data. Generally, in various embodiments, guidedde-noising of a second image using a first image may include applying afilter to the second image, with the filter least in part being definedas a function of the first image. For example, a weighting applied to agiven pixel of the second image may be a function of a correspondingpixel or group of pixels of the first image.

Various embodiments provide improved imaging, e.g. via improvedde-noising or noise reduction. A technical effect of at least oneembodiment includes reduced noise in material decomposition imagesand/or reduced noise in synthetic monochromatic images formed frommaterial decomposition images. A technical effect of at least oneembodiment includes providing reduced noise with relatively lowcomputational requirements. A technical effect of at least oneembodiment includes elimination or reduction of cross-contamination ofnoise between MD images and monochromatic images relative toconventional techniques. A technical effect of at least one embodimentincludes providing improved noise reduction across all energies of arange. A technical effect of at least one embodiment includes allowingthe use of lower radiation dosages for scans without compromising imagequality or diagnostic capability (e.g., due to improved noisereduction). A technical effect of at least one embodiment includesavoidance or reduction of image artifacts, resolution loss, and/or otherimage degradations associated with conventional MD de-noisingtechniques. A technical effect of at least one embodiment includesrecovery of at least a portion of flux loss associated with acquiringdual-energy data on scanners with limited tube capability or withdecreased flux efficiency.

FIG. 1 illustrates a schematic process flow 100 for guided de-noising inaccordance with various embodiments. It may be noted that the variousboxes depicted in FIG. 1 may represent process steps in some embodimentsand/or components or aspects configured to perform process steps in someembodiments. Generally, as seen in FIG. 1, dual-energy CT informationmay be received as an input in various embodiments, and an output ofde-noised material images and/or a de-noised monochromatic image (orimages) may be provided as an output of the process flow 100. In variousembodiments, certain blocks may be omitted, and/or additional processblocks may be added (see, e.g., FIG. 5).

For the embodiment depicted in FIG. 1, at block 110, a filtered backprojection is performed on dual-energy CT information acquired during ascan. In the illustrated embodiment, high-energy information E₁ andlow-energy information E₂ are used to generate material decompositionimages, specifically a first raw material decomposition image m₁ and asecond raw material decomposition image m₂. By way of example, the firstmaterial decomposition image m₁ may correspond to an iodine image andthe second material decomposition image m₂ may correspond to a waterimage. As part of a process including guided de-noising illustrated bythe dashed lines defining block 120, the raw material decompositionimages m₁ and m₂ may be de-noised to provide corresponding de-noised MDimages {circumflex over (m)}₁ and {circumflex over (m)}₂.

In the illustrated embodiment, the block 120 includes a lineartransformation block (block 130), a guided de-noising block (block 140),and an inverse linear transformation block (block 150). The lineartransformation performed at block 130 is configured to provide alow-noise image and a high-noise image, with the low-noise image laterused for guided de-noising of the high-noise image. The low-noise imagemay be provided via a first linear transform and the high-noise imagevia a second linear transform. In some embodiments, the second lineartransform may be a transpose of the first linear transform. The lineartransformation performed at block 130 may provide first and secondmonochromatic images in some embodiments; however, other transforms maybe employed in other embodiments. In some embodiments, the high-noiseimage may be a transpose of the low-noise image. It may be noted that,as used herein, the first linear transform and second linear transformneed not necessarily be separate, but may each be a portion or aspect ofa common linear transform that includes both the first linear transformand the second linear transform. A transform, for example, may beapplied to a set of two images to produce another set of two images,with the first linear transform being a portion of the transform used toproduce the first image and the second linear transform being a portionused to produce the second image. (See, e.g., Equations (1) and (2).)Similarly, an inverse transform may be have two portions, with the firstportion referred to as a first inverse transform and the second portionor aspect referred to as a second inverse transform.

In the illustrated embodiment, at block 130, a first lineartransformation is performed to provide a first basis image x₁ and asecond linear transformation is performed to provide a second basisimage x₂ from the first raw material decomposition image m₁ and thesecond raw material decomposition image m₂. The first basis image x₁ isa low-noise image, and the second basis image x₂ is a high-noise image.The particular transforms employed in some embodiments are configured toprovide a minimum or reduced noise level in the first basis image and amaximum, elevated, or increased noise level in the second basis image.For example, the first and second basis images may be syntheticmonochromatic images generated from the first and second materialimages, but the noise level of the first basis image may be lower than anoise level for a monochromatic image generated for diagnostic purposesusing the first and second material images, while the noise level of thesecond basis image may be higher than a noise level for a monochromaticimage generated for diagnostic purposes using the first and secondmaterial images.

For example, the basis images may be monochromatic images generated atan optimal energy or keV level. As used herein, an optimal energy levelor an optimal keV level may be understood as an energy level at which anoise level for the first linear transform is minimized relative toother energy values. The optimal energy level may correspond to the meanenergy of an output spectrum after attenuation by the object beingimaged. The particular relationship between optimal energy and theoutput spectrum may vary by system and object being imaged. Generally,in various embodiments, conventional techniques to determine the optimalenergy level for a given system and object being imaged with the systemmay be employed. At the optimal energy, the linear transformationperformed at block 130 may include an addition that generates thelow-noise image x₁ and a subtraction used to generate the high-noiseimage x₂. For example, in the illustrated embodiment, the additionremoves negatively correlated noise to provide a low noise image and thesubtraction enhances negatively correlated noise to provide a high noiseimage. It may be noted that, in alternate embodiments, an energy levelother than the optimal energy may be employed with the lineartransformation.

Next, at block 140, the low noise first basis image x₁ is used tode-noise the high noise second basis image x₂ to provide a de-noisedsecond basis image {circumflex over (x)}₂. For guided de-noising of thesecond basis image x₂ using the first basis image x₁, a filter that isat least in part defined by the first basis image x₁ may be applied tothe second basis image x₂. For example, a weighting may be applied topixels of the second basis image x₂, with at least one term of theweighting varying as a function of one or more corresponding pixels ofthe first basis image x₁.

Following the de-noising of the second basis image x₂, an inverse lineartransformation (e.g., an inverse of the transformation previouslyperformed at 130) is applied to the first basis image x₁ and thede-noised second basis image {circumflex over (x)}₂ at 150 to provide afirst de-noised material image {circumflex over (m)}1 and a secondde-noised material image {circumflex over (m)}2. For example, the firstde-noised material image {circumflex over (m)}1 may be an iodine imageand the second de-noised material image {circumflex over (m)}2 may be awater image. The de-noised material images may be an end product of theprocess and may be displayed to a user. Alternatively or additionally,further processing of the de-noised material images (e.g., the output ofblock 120) may be performed. For example, at block 160 of theillustrated embodiment, a linear transform is performed using the firstde-noised material image {circumflex over (m)}1 and the second de-noisedmaterial image {circumflex over (m)}2 to provide a syntheticmonochromatic image Î. One or more synthetic monochromatic images may begenerated using the de-noised material images at one or more desiredenergies. It may be noted that, in some embodiments (see, e.g., FIG. 5and related discussion), other processing or de-noising may be performedbefore performance of the inverse linear transformation at 150.

Various different transforms may be used in keeping with the generalprinciples outline herein. For example, in some embodiments, a guidedde-noising approach applies the principle that there exists a particularphoton energy at which the corresponding linear combination of raw MDimages produces a monochromatic image with minimal noise across alldiagnostically interesting energy levels. That is, the negativelycorrelated noise in the two MD images cancels at this energy. Theparticular photon energy may be referred to herein as the optimal keVlevel, as also discussed elsewhere herein. In various embodiments, amonochromatic image at the optimal keV level may be used as the guide toperform guided de-noising.

For example, m₁ may be a first material decomposition image and m₂ maybe a second material decomposition image. In various embodiments, theraw decomposition images may be water and iodine images generated usinga back filtered projection of dual energy CT information acquired duringa scan. Also x₁ and x₂ may be the images resulting from a lineartransformation T (e.g., a linear transformation that may be performed atblock 130). The linear transformation T in various embodiments may begiven by:

$\begin{matrix}{\begin{bmatrix}x_{1} \\x_{2}\end{bmatrix} = {{T\begin{bmatrix}m_{1} \\m_{2}\end{bmatrix}} = {\begin{bmatrix}\alpha_{1} & \alpha_{2} \\\beta_{1} & \beta_{2}\end{bmatrix}\begin{bmatrix}m_{1} \\m_{2}\end{bmatrix}}}} & ( {{Eq}.\mspace{14mu} 1} )\end{matrix}$

As indicated above, x₁ in various embodiments is the optimal keVmonochromatic image, where α₁ and α₂ represent the coefficients formonochromatic combination at the optimal keV level. If (β₁, β₂)=(α₁,−α₂), then the following results:

$\begin{matrix}{\begin{bmatrix}x_{1} \\x_{2}\end{bmatrix} = {\begin{bmatrix}\alpha_{1} & \alpha_{2} \\\alpha_{1} & {- \alpha_{2}}\end{bmatrix}\begin{bmatrix}m_{1} \\m_{2}\end{bmatrix}}} & ( {{Eq}.\mspace{14mu} 2} )\end{matrix}$

For this particular transformation, the correlated noise cancels in theoptimal keV monochromatic image, x₁, while the correlated noise ismagnified in the other image x₂. Accordingly, guided de-noising may beemployed to remove the correlated noise in x₂ using x₁ as a guide.

Various de-noising filters may be employed for guided de-noising inconnection with de-noising algorithms, techniques, processes and/orsystems in accordance with various embodiments. Generally, severalconsiderations may be taken into account when specifying a particularde-noising filter. First, a de-noising filter is configured to use theinformation from the guide image to effectively remove the correlatednoise in the image to be de-noised, while still preserving true signalin the noisy image. Further, the de-noising filter should not introduceartificial structures from the guide image to the noisy image that isde-noised. Additionally, it is beneficial for the de-noising filter tobe as computationally simple and efficient as possible.

In one example, a trilateral filter may be employed as a de-noisingfilter to accomplish the above discussed goals. The example trilateralfilter calculates each pixel value in the filtered output as a linearcombination of pixel values of neighboring pixels, where the combinationweights are generated based on spatial distances, intensity similaritiesin the guide image, and intensity similarities in the image to bede-noised, between the objective pixel and neighboring pixels.Specifically, for one example trilateral filter, the filtered value ofx₂ at pixel i is given by:

$\begin{matrix}{{{\hat{x}}_{2,i} = {\sum\limits_{j}\; {w_{ij}x_{2,j}}}},} & ( {{Eq}.\mspace{14mu} 3} )\end{matrix}$

The weight, w_(ij), for the example filter is given by:

$\begin{matrix}{w_{ij} = {\frac{1}{z_{i}}\exp \{ {{- \frac{( {i - j} )^{2}}{\sigma_{s}^{2}}} - \frac{( {x_{1,i} - x_{1,j}} )^{2}}{\sigma_{r}^{2}} - \frac{( {{\overset{\_}{x}}_{2,i} - {\overset{\_}{x}}_{2,j}} )^{2}}{\sigma_{q}^{2}}} \}}} & ( {{Eq}.\mspace{14mu} 4} )\end{matrix}$

In Equation 4, σ_(s), σ_(r), and σ_(q) are filter parameters, andz_(i)=Σ_(j)w_(ij) is a normalizing factor.

For the example trilateral filter, the terms within the exponentialserve different functions. For example, the first term in theexponential (−(i-j)²/σ_(s) ²), is configured to account for thegeometric similarity between pixel I and j, which decreases as thespatial distance of two pixels increases and therefore reduces theinfluence of the filter as pixels are further apart. The first termprovides an example of a component of a filter that varies as a functionof a distance between pixels.

The second term, (−(x_(1,i)-x_(1,j))²/σ_(r) ²), is configured to accountfor photometric similarities in the guide image, x₁. This term providesfor guiding of the filtering of the image x₂ using the image x₁. Forexample, this term may help identify portions of the guide image thatcorrespond to an edge or to a homogenous region. In the example filter,actual pixel values for the particular pixels are used for this term.The second term provides an example of a component of a filter thatvaries as a function of information from the first basis image (or guideimage).

The third term, (−(x _(2,i)-x _(2,j))²/σ_(q) ²), is configured toaccount for photometric similarities in the locally averaged noiseimage, x ₂. The third term provides an example of a component of afilter that varies as a function of information from a second basisimage (or image to be de-noised with a guided filter). In the examplefilter, locally averaged values are used for this term. It may be notedthat, for example, actual values of pixels instead of locally averagedvalues may be used in alternate embodiments. For the second and thirdterms, the value decreases as the intensity difference of two pixels incorresponding images increases, which helps provide smoothing only amongpixels with similar intensities.

For the example filter, the second and third terms together reflect theuse of guided de-noising using information from both the guide image andthe image to be de-noised. For example, x₁ (an optimal keV image, forexample) provides a signal with good or high confidence that thereforemay be used as a guide, while x₂ (the noisy image), though corruptedwith a relatively high noise level, may still contain relatively weakbut nevertheless unique information for material differentiation, andtherefore should not be ignored. Thus, both the low-noise (or guide)image and the noisy image (or image to be de-noised) may be consideredfor generation of adequate weights for the de-noising filter, but withdifferent confidence levels, for example σ_(r)<σ_(q). Thus, with ahigher confidence in the guide image (e.g., σ_(r)<σ_(q)), the weightw_(ij) mainly depends on the optimal keV image, x₁, but is slightlymodulated by a locally averaged version of the noisy image, x ₂, whichis a simple estimate of the mean.

The strengths (e.g., the relative strengths) of the parameters (σ_(s),σ_(r), σ_(q)) may be tailored to achieve desired performance of thetrilateral filter. For example, the parameter σ_(s) controls thestrength of spatial smoothing, with a larger σ_(s) making the spatialkernel flatter. The parameters σ_(r) and σ_(q) control the influence ofthe guide image and the noisy image, respectively. Larger σ_(r) andσ_(q) introduces more smoothing, while a smaller σ_(r)/σ_(q) ratioincreases the influence of the guide image over the filtered noisyimage. In various embodiments, the values of the parameters and/or theparticular configuration of terms used may be varied, for example toprovide a desired image quality, amount of de-noising, and/orcomputational efficiency.

It may be noted that the above discussed trilateral filter provides anexample of a filter that has a first component, a second component, anda third component, wherein the first component varies as the function ofthe information from the first basis image, the second component variesas a function of information from the second basis image, and the thirdcomponent varies as a function of a distance between pixels. In otherembodiments, fewer or more components or terms may be employed. Forexample, in some embodiments, the first component, but not the secondand third components, may be utilized, or, as another example, in someembodiments, the first and second components, but not the thirdcomponent, may be employed.

FIG. 2 illustrates results of an example guided de-noising in accordancewith various embodiments, and FIG. 3 provides a comparison of resultsbetween conventional de-noising and guided de-noising in accordance withvarious embodiments. For the example depicted in FIG. 2, a guidedde-noising algorithm employing a trilateral filter as described abovewas employed using the following parameter settings. The local averagednoise image, x ₂, was produced by filtering the original noisy image,x₂, with a 7×7 Gaussian filter with a standard deviation of 2. σ_(s) wasset to 9, for all cases, and σ_(r) was set at 20 for a high dose caseand at 80 for a low does case. Further, the parameter σ_(q) was set bythe following equation:

$\begin{matrix}{\sigma_{q} = {\min \{ {{\max \{ {\frac{5\sigma_{r}^{2}}{\sigma_{p}},{2\sigma_{r}}} \}},{4\sigma_{r}}} \}}} & ( {{Eq}.\mspace{14mu} 5} )\end{matrix}$

In Equation 5, σ_(p) is the local standard deviation in the guide imagecalculated by using a 7×7 sliding window. In this example, for a localregion with larger variation in the guide image, the filtered noisyimage contributes with higher influence than in other regions, in orderto help preserve edges or structures. The lower bound of σ_(p) restrictsthe influence of the noisy image to ensure sufficient noise reduction,while the upper bound of σ_(p) ensures certain contribution from thenoisy image to recover unique signal that is missing in the guide image.

For the example of FIGS. 2 and 3, a low dose clinical abdominal scan wasperformed. FIG. 2 shows the input and output of the trilateral filter.In FIG. 2, a guide image 210 used for de-noising an input noisy image220 is shown. In the illustrated embodiment, the guide image 210 is a 70keV synthetic monochromatic image, and the input noisy image 220 wasgenerated from subtraction of material decomposition images. An outputde-noised image 230 is also shown in FIG. 2. The output de-noised image230 and input guide image 210, for example, may be utilized to generatede-noised material decomposition images, which may in turn be utilizedto generate a de-noised monochromatic image. FIG. 3 shows a comparisonbetween conventional de-noising results and results of an exampleembodiment. Image 310 is a water image obtained via conventionaltechniques, and image 320 is an iodine image obtained via conventionaltechniques. Also, image 330 is a water image obtained using guidedde-noising in accordance with various embodiments, and image 340 is aniodine image obtained using guided de-noising in accordance with variousembodiments. As seen in FIG. 3, the images 330 and 340 have lower noiseand better texture without compromise of spatial resolution, incomparison with the images 310 and 320.

FIG. 4 illustrates a graph 400 comparing noise measured in monochromaticimages across different photon energies that have been synthesized fromMD images. In FIG. 4, the first curve 410 depicts noise in raw (e.g.,not de-noised) synthesized monochromatic images, the second curve 420depicts noise in synthesized monochromatic images produced usingconventional de-noising techniques, and the third curve 430 depictsnoise in synthesized monochromatic images de-noised using guidedde-noising in accordance with an example embodiment. The noisemeasurements of the graph 400 were taken within a homogeneous regionwith a liver. As seen in FIG. 4, the monochromatic images de-noisedusing guided de-noising contain consistently lower noise than those fromthe raw or conventionally de-noised images across the range of the keVspectrum.

As also mentioned above, various aspects of algorithms, processes, orsystems for de-noising may be varied for different embodiments. Forexample, instead of the transform described by Equation 1 above, adifferent transform may be employed. In some embodiments, the followingorthogonal transformation may be utilized:

$\begin{matrix}{T = \begin{bmatrix}\alpha_{1} & \alpha_{2} \\\alpha_{2} & {- \alpha_{1}}\end{bmatrix}} & ( {{Eq}.\mspace{14mu} 6} )\end{matrix}$

As another example, the photon energy of synthesized monochromaticimages may differ from the optimal keV for all or a portion of atransform. For example, the following transform may be employed invarious embodiments:

$\begin{matrix}{T = \begin{bmatrix}{\alpha_{1}( \varepsilon_{opt} )} & {\alpha_{2}( \varepsilon_{opt} )} \\{\alpha_{1}(\varepsilon)} & {\alpha_{2}(\varepsilon)}\end{bmatrix}} & ( {{Eq}.\mspace{14mu} 7} )\end{matrix}$

In Equation 7, ε_(opt) is the optimal photon energy and ε is a differentphoton energy. A further possible transformation that may be used togenerate basis images is to work directly from the low-energy andhigh-energy kVp images instead of the material images to form the guideimage. For example, the linear combination of low and high kVp imagesmay provide a computationally cheaper alternative for producing a guideimage relative to using the material images, which require a moretime-consuming MD (material decomposition) process, and which may alsorequire more data.

As one more example, the image where guided de-noising takes place maybe a material image, instead of applying a transformation (e.g., thetransformation of Equation 1). In such a case, the principle of negativecorrelation in noise between images is not relied upon, but guidedde-noising may still be effective and/or provide for improvedde-noising. Further still, guided de-noising may be performed in asingle kVp domain.

Further still, the various parameters of a trilateral filter may beadjusted for various embodiments. For example, the parameter σ_(s)controls the strength of spatial smoothing, with larger valuesintroducing heavier smoothing. In the above discussed example, theparameter σ_(s) was fixed. However, the parameter may not be fixed inother embodiments. For example, the parameter may be designed asfunction of the actual voxel size. In some embodiments, a smaller σ_(s)may be used for images with larger voxel size, or for a largerfield-of-view (FOI), to reduce or avoid loss of detail. Further, theparameter may also be designed as adaptive to actual noise level of theoptimal keV image, with a larger value for higher noise level, as highernoise level may also have stronger spatial correlation and consequentlybenefit from heavier spatial smoothing.

Further still, the windowing due to the infinite support of Gaussianfunction may be considered when configuring σ_(s). For example, in someembodiments, a 2D rectangular window may be utilized to cover a range of[−3σ_(s), +3σ_(s)] in both dimensions (e.g., a 55×55 rectangular windowmay be used for x=σ_(s)). Though such a large window may avoid spectralleakage, such a large window may include an unnecessarily large numberof zero elements that may potentially increase computational expense. Insome embodiments, the potentially increased computational expense may beaddressed via use of a 2D hamming/hanning window, which provides asmaller window but still avoids or reduces spectral leakage.

As another example, the parameter σ_(r) controls the smoothing strengthrelated to intensity of the guide signal, with large values introducingmore smoothing across different intensities. In some embodiments, asdiscussed herein, the parameter σ_(r) may vary among different caseswith different dose levels, and may be empirically determined. Forexample, as discussed above, σ_(r)=20 may be used for high dose casesand σ_(r)=80 for low dose cases. In various embodiments, however, σ_(r)may be configured as explicitly related to the noise level in theoptimal keV signal. Such a configuration may require an estimation ofthe noise level. Further, σ_(r) may be configured as spatially varyingadaptive to local variation in the guide signal. For example, a smallerσ_(r) may be used for a local region with larger variation that mayindicate an edge to avoid over-smoothing.

As yet another example, the parameter σ_(q) controls the smoothingstrength related to intensity of the noisy signal, which also controlsthe influence of the noisy signal over the guide signal. Larger valuesof σ_(q) introduce more smoothing across different intensities, whilelarger σ_(r)/σ_(q) ratios reduce the influence of the noisy signal overthe guide signal. Potential variations in the configuration of σ_(q)include use of different upper and lower bounds for σ_(q) (for example,upper and lower bounds for Eq. 5), and/or varying a window size forcalculating σ_(q) based on actual voxel size and/or noise level.

Yet further still, in various embodiments, different techniques forcalculating x ₂ may be employed. As discussed above, x ₂ is an estimateof the mean of the noisy signal, and used in conjunction withcalculation of the weights of the trilateral filter. In an abovediscussed example, a 7×7 Gaussian filter with a standard deviation of 2was used to obtain a local average of the noisy signal. However, inalternate embodiments, the filter size and strength, for example, may beproportional to the noise level and/or inversely proportional to theactual voxel size.

In some embodiments, a 3D de-noising filter (e.g., 3D trilateral filter)may be employed. Use of a 3D filter may provide improved noise reductionand detail preservation, for example because edges tend to have betterspatial correlation than noise in 3D. However, use of a 3D filter mayincrease computational requirements.

Further still, the filter design may include fewer terms than specifiedby the example trilateral filter discussed herein, or may includeadditional constraints based on desired relationships between x₁, x₂,and x ₂. Also, the weight computation terms may include othermathematical components than the exponentials described in Equation 4.For example, other mathematical techniques may be employed to multiplythe components together to form the filter weights, thus modifying theform of the disclosed trilateral filter to other general filterformulations with multi-dimensional inputs.

It may also be noted that additional de-noising and/or other forms ofprocessing of basis images used in guided de-noising may be utilized invarious embodiments. FIG. 5 illustrates a schematic process flow 500 forguided de-noising in accordance with various embodiments. It may benoted that the various boxes depicted in FIG. 5 may represent processsteps in some embodiments and/or components or aspects configured toperform process steps in some embodiments. The process flow 500 may begenerally similar to the flow 100 discussed herein in connection withFIG. 1, with like numbered or labelled aspects being generally similar;however, the process flow 500 includes additional processing blocks usein conjunction with the first basis image x₁ or guide image (e.g., theimage used as a guide in de-noising a different image).

In the embodiment depicted in FIG. 5, the process flow 500 includes ablock 510 and a block 520 in addition to blocks already discussed hereinin connection with FIG. 1. For the blocks 510 and 520, additionalde-noising, other filtering, and/or other processing may be performed.At block 510, a filtering process is performed on the guide image x₁ toprovide a modified guide image {circumflex over (x)}₁. The modifiedguide image {circumflex over (x)}₁ is provided to block 520 foradditional de-noising before use as a guide for guided de-noising of x₂to provide {circumflex over (x)}₂. Additionally, the modified guideimage {circumflex over (x)}₁ is provided along with the de-noised image{circumflex over (x)}₂ to block 150, where an inverse lineartransformation is performed using {circumflex over (x)}₁ and {circumflexover (x)}₂ to provide de-noised material images (e.g., water and iodineimages). It may be noted that the additional processing (e.g., performedat block 510 and/or at block 520) may additionally or alternativelyinclude processing other than de-noising, such as edge enhancing orGaussian smoothing, among others.

For the illustrated embodiment, at block 510, additional filtering forthe guide image or guide signal is performed. For example, the block 510may correspond to an edge-preserving filter. The filtering at block 510may beneficially suppress uncorrelated noise in the guide image orsignal and consequently lead to MD images with reduced noise levels. Asshown in FIG. 5, the result of block 510 is also utilized by block 150in performing the inverse linear transform that provides the materialimages. Accordingly, the filter performed at block 510 may be configuredto avoid or minimize any loss of actual signal and to avoid or minimizecreation of artifacts.

Also for the illustrated embodiment, an additional filter may beprovided as shown at block 520. The filter at block 520, for example,may be configured as a simple smoothing filter. The additional filter atblock 520 in the illustrated embodiment receives a guide signal or image(or modified guide signal or image, for example, for embodimentsincluding block 510 as shown in FIG. 5), and produces a smoothed signalas an output, with the output of block 520 used to guide de-noising ofthe noisy signal at block 140. The depicted filter 520 is utilized forguided de-noising but not in connection with the inverse lineartransform for producing the material images performed at block 150. Thefiltering performed at block 520 may provide slight smoothing in theguide signal to help with the de-noising performance of the filteringperformed at 130; however, since the filtering performed at block 520has no direct impact on the material images generated by block 150, thefiltering may be configured as a simple linear smoothing, for example, asmall Gaussian kernel.

The filtering performed at block 510, as well as the filtering performedat block 520, provide examples of performing additional processing onthe first basis image to provide a modified basis image for use as partof guided de-noising. Also, the filtering performed at block 510provides an example of performing additional processing on the firstbasis image before performing the first and second inverse lineartransforms to provide the material images. Other combinations orpositions of additional processing blocks may be utilized in variousembodiments.

FIG. 6 provides a flowchart of a method 600 for imaging an object, inaccordance with various embodiments. The method 600, for example, mayemploy or be performed by structures or aspects of various embodiments(e.g., systems and/or methods and/or process flows) discussed herein. Invarious embodiments, certain steps may be omitted or added, certainsteps may be combined, certain steps may be performed simultaneously,certain steps may be performed concurrently, certain steps may be splitinto multiple steps, certain steps may be performed in a differentorder, or certain steps or series of steps may be re-performed in aniterative fashion. In various embodiments, portions, aspects, and/orvariations of the method 600 may be able to be used as one or morealgorithms to direct hardware (e.g., one or more aspects of theprocessing unit 720) to perform one or more operations described herein.

At 602, spectral CT information is acquired using a CT acquisition unitthat includes an X-ray source and detector that rotate about an objectto be imaged (e.g., on a gantry). In some embodiments, the X-ray source(or sources) is configured to produce X-rays at more than one energy(e.g., to produce at least two spectrums of energy having differentmaximum energies). In various embodiments, the X-ray source may beconfigured for dual-energy CT and provide X-rays at a first, higherenergy level and at a second, lower energy level. For example,dual-energy CT information may be acquired in some embodiments byalternating tube energy at different rotational positions to provideinterleaved information (e.g., alternating tube energy at every otherrotational position of a rotating CT gantry). In other embodiments, oneenergy level may be employed for a complete rotation and a differentenergy level employed for a subsequent rotation. As another example, twoor more X-ray tubes providing different energies, along withcorresponding detectors, may be used. As one more example, detectors maybe utilized that absorb one range of energy while passing other rangeson to other detectors to acquire spectral CT information.

At 604, basis images are generated. For example, one basis image havinga relatively lower impact of one or more artifacts may be generated, andanother basis image having a relatively higher impact of one or moreartifacts may be generated. For instance, in the depicted embodiment,the basis images are generated to provide one basis image having aminimal or low noise level (a guide image) and another basis imagehaving a maximum, elevated, or high noise level (a noisy image to bede-noised). The basis images are also configured such that each basisimage contains at least some useful information not contained in theother basis image. It may be noted that for dual-energy scans, thehigh-energy projection data typically is less noisy than the low-energyprojection data. Accordingly, in one example, high-energy data may beused for a first basis image or guide image, and low-energy data may beused for a second basis image to be de-noised using a guided de-noisingtechnique as discussed herein. In some embodiments, dual-energy CTinformation may be further processed to provide basis signals, forexample using one or both of substeps 606 and 608 depicted in FIG. 6.

At 606 of the illustrated embodiment, material images are generated, forexample using a filtered back projection (see, e.g., block 110 of FIG. 1and related discussion). The material images may be generated based onthe different variations of attenuation as a function of X-ray energyexhibited by different materials. For example, a water image and aniodine image may be generated. In some embodiments, the material imagescan be used as basis images, while in other embodiments the materialimages may be further processed to generate basis images for guidedde-noising. In the illustrated embodiment, at 608, a lineartransformation is performed (see, e.g., block 130 of FIG. 1 and relateddiscussion) to provide a low-noise basis image and a high-noise basisimage. For example, one or both basis images generated by the lineartransformation may be a synthetic monochromatic image at an optimalenergy level. In some embodiments, a linear transform used to generatethe high-noise basis image may be a transpose of a linear transform usedto generate the low-noise basis image. In some embodiments, thelow-noise basis image may be generated by an additive operation, and thehigh-noise image may be generated by a subtractive operation.

At 610, guided de-noising is performed (see, e.g., block 140 of FIG. 1and related discussion). In the depicted embodiment, the high-noisebasis image is de-noised using the low-noise basis image as a guide. Forexample, a filter including one or more components may be employed, withat least one component of the filter using information from thelow-noise basis image to de-noise the high-noise basis image. The filterin some embodiments may be configured as a trilateral filter asdiscussed herein. It may be noted that additional processing steps maybe performed on the low-noise basis image and/or the high-noise basisimage to further reduce noise and/or improve image quality (see, e.g.,blocks 510 and 520 of FIG. 5 and related discussion). The guidedde-noising provides a de-noised or modified high-noise basis image.

At 612, one or more images are generated, for example, for display to auser. In some embodiments, the de-noised image may be displayed to auser. Additionally or alternatively, the de-noised image may be used togenerate one or more additional images. The de-noised image may be usedin conjunction with the low-noise basis image (or a further processed ormodified version thereof) to generate one or more final images. In theillustrated embodiment, at 614, an inverse linear transform (see, e.g.,block 150 of FIG. 1 and related discussion) to provide material imageswhich may be displayed. Additionally or alternatively, the materialimages may be used to generate one or more synthetic monochromaticimages (e.g., a monochromatic image at a predetermined energy level,such as the optimal energy level). For example, in the illustratedembodiment, at 616, a linear transform is performed using the first orlow-noise basis image along with the second or de-noised basis image toprovide a synthetic monochromatic image (see, e.g., block 160 of FIG. 1and related discussion).

FIG. 7 illustrates an imaging system 700 in accordance with anembodiment. The imaging system 700 may be utilized to perform orimplement one or more aspects of the process flow 100, the process flow500, and/or the method 600 discussed herein, for example. The imagingsystem 700 may be configured, for example, to perform computedtomography (CT) scanning of an object, such as a human or animal patient(or portion thereof. The imaging system 700 includes a CT acquisitionunit 710 and a processing unit 720. Generally, the CT acquisition unit710 is configured to acquire projection data or imaging data at two ormore energy levels (e.g., spectral CT data or spectral CT imaginginformation), and the processing unit 720 is configured to reconstructimages using the data acquired by the CT acquisition unit 710. It may benoted that various embodiments may include additional components, or maynot include all of the components shown in FIG. 7 (for example, variousembodiments may provide sub-systems for use with other sub-systems toprovide an imaging system). Further, it may be noted that certainaspects of the imaging system 700 shown as separate blocks in FIG. 7 maybe incorporated into a single physical entity, and/or aspects shown as asingle block in FIG. 7 may be shared or divided among two or morephysical entities.

The depicted CT acquisition unit 710 includes an X-ray source 712 and aCT detector 714. (For additional information regarding example CTsystems, see FIG. 8 and related discussion herein.) The X-ray source 712and the CT detector 714 (along with associated components such as bowtiefilters, source collimators, detector collimators, or the like (notshown in FIG. 7)) may rotate about a central axis of a bore of a gantry716 of the system 700.

Generally, X-rays from the X-ray source 712 may be guided to an objectto be imaged through a source collimator and bowtie filter. As discussedherein, the X-ray source is configured to provide X-rays at at least twodifferent energy levels. The object to be imaged, for example, may be ahuman patient, or a portion thereof (e.g., head or torso, among others).The source collimator may be configured to allow X-rays within a desiredfield of view (FOV) to pass through to the object to be imaged whileblocking other X-rays. The bowtie filter module may be configured toabsorb radiation from the X-ray source 712 to control distribution ofX-rays passed to the object to be imaged.

X-rays that pass through the object to be imaged are attenuated by theobject and received by the CT detector 714 (which may have a detectorcollimator associated therewith), which detects the attenuated X-raysand provides imaging information to the processing unit 720. Theprocessing unit 720 may then reconstruct an image of the scanned portionof the object using the imaging information (or projection information)provided by the CT detector 714. The processing unit 720 may include orbe operably coupled to the output unit 740, which in the illustratedembodiment is configured to display an image, for example, one or morematerial (e.g., iodine or water) images and/or one or more syntheticmonochromatic images generated by the processing unit 720 using imaginginformation from the CT detector 714. The depicted input unit 750 isconfigured to obtain input corresponding to a scan to be performed, withthe processing unit 720 using the input to determine one or more scansettings (e.g., tube voltage, tube current, scanning rotation speed, orthe like). The input unit 750 may include a keyboard, mouse, touchscreenor the like to receive input from an operator, and/or may include a portor other connectivity device to receive input from a computer or othersource.

In the illustrated embodiment, the X-ray source 712 is configured torotate about the object. For example, the X-ray source 712 and the CTdetector 714 may be positioned about a bore 718 of the gantry 716 androtated about the object to be imaged. As the X-ray source 712 rotatesabout the object during an imaging scan, X-rays received by the CTdetector 714 during one complete rotation provide a 360 degree view ofX-rays that have passed through the object. Other imaging scanningranges may be used in alternative embodiments. The CT imaginginformation may be collected as a series of views that together make upa rotation or portion thereof. Each view or projection may have a viewduration during which information (e.g., counts) is collected for theparticular view. The view duration for a particular view defines a CTinformation acquisition period for that particular view. For example,each rotation may be made up of about 1000 views or projections, witheach view or projection having a duration or length of about 1/1000 of acomplete rotation. The X-ray source 712 may be alternated between a highenergy level and a lower energy level at alternating views orprojections to collect dual energy CT information. In variousembodiments, other arrangements may be utilized to collect spectral CTinformation (see, e.g., discussion herein in connection with step 602 ofmethod 600).

As indicated herein, the processing unit 720 is configured to controlvarious aspects of the acquisition units and/or to reconstruct an imageusing information obtained via the acquisition units. For example, theprocessing unit 720 may be configured to reconstruct a CT image usinginformation collected by the CT acquisition unit 710. The processingunit 720 of the illustrated embodiment is configured to perform one ormore aspects discussed in connection with process flow 100, process flow500, or method 600 (e.g., generation of high and low energy projections,generation of material decomposition images, generation of basis images,processing of basis images, guided de-noising of a high-noise basisimage using a low-noise basis image, performing an inverse lineartransformation to provide de-noised basis images or de-noised materialimages, or performing a linear transformation to provide a de-noisedsynthetic monochromatic image, among others).

The depicted processing unit 720 is operably coupled to the input unit750, the output unit 740, and the CT acquisition unit 710. Theprocessing unit 720, for example, may receive imaging data or projectiondata from the CT detector 714 (e.g., dual-energy CT projection data). Asanother example, the processing unit 720 may provide control signals toone or more aspects of the CT acquisition unit 710, such as the X-raysource 712 and CT detector 714. The processing unit 720 may includeprocessing circuitry configured to perform one or more tasks, functions,or steps discussed herein. It may be noted that “processing unit” asused herein is not intended to necessarily be limited to a singleprocessor or computer. For example, the processing unit 720 may includemultiple processors and/or computers, which may be integrated in acommon housing or unit, or which may distributed among various units orhousings. It may be noted that operations performed by the processingunit 720 (e.g., operations corresponding to process flows or methodsdiscussed herein, or aspects thereof) may be sufficiently complex thatthe operations may not be performed by a human being within a reasonabletime period. For example, the processing of imaging data, control of animaging acquisition unit, or performance of filtering, back projection,linear transforms, or inverse linear transforms may rely on or utilizecomputations that may not be completed by a person within a reasonabletime period.

The depicted processing unit 720 is configured to control the CTacquisition unit 710 to collect dual-energy CT information during animaging scan.

In the illustrated embodiment, the processing unit includes a materialdecomposition module 722, a linear transformation module 723, a guidedde-noising module 724, a control module 726, and a memory 728. It may benoted that other types, numbers, or combinations of modules may beemployed in alternate embodiments, and/or various aspects of modulesdescribed herein may be utilized in connection with different modulesadditionally or alternatively. Generally, the various aspects of theprocessing unit 720 act individually or cooperatively with other aspectsto perform one or more aspects of the methods, steps, or processesdiscussed herein.

The depicted material decomposition module 722 is configured to acquireCT projection data from the CT acquisition unit 710, and to perform amaterial decomposition to provide first and second material images. Forexample, the material decomposition module 722 may be configured toemploy a filtered back projection along with predetermined informationregarding the change of attenuation for materials with energy level togenerate the first and second material images. In some embodiments, oneof the material images may be a water image and another material imagemay be an iodine image.

The depicted linear transformation module 723 is configured to performvarious linear transformations and inverse linear transformations, suchas those discussed elsewhere herein. For example, the lineartransformation module 723 of the illustrated embodiment is configured toacquire the material images from the material decomposition module 722and to generate a high-noise basis image and a low-noise basis imageusing the material images. As another example, the linear transformationmodule 723 may acquire one or more de-noised or otherwise modified basisimages from the guided de-noising module 724, and perform an inversetransform to provide de-noised material images. Further still, thelinear transformation module 723 may perform a linear transformationusing de-noised material images to provide one or more de-noisedsynthetic monochromatic image. In some embodiments, the lineartransformation module 723 may use a similar transform for generating abasis image (e.g., a low-noise or guide basis image) from a materialimage and a de-noised monochromatic image from a de-noised materialimage, while in other embodiments the linear transformation module 723may use different transforms to generate basis images and de-noisedmonochromatic images.

The depicted guided de-noising module 724 is configured to performguided de-noising as described herein. In the illustrated embodiment,the guided de-noising module 724 acquires a first, low-noise or guidebasis image, along with a second, high-noise basis image from the lineartransformation module 723. The guided de-noising module 724 subsequentlyuses the first basis image as a guide for de-noising the second basisimage to provide a de-noised or modified second basis image. Forexample, the guided de-noising module may apply, to the second basisimage, a filter that includes a weighting that varies as a function ofinformation from the first basis image. The filter may be a trilateralfilter as discussed herein. The guided de-noising module 724 may performadditional processing steps on one or more basis images as discussedherein (see, e.g., FIG. 5 and related discussion).

The depicted control module 726 is configured to control the CTacquisition unit 710 and/or other aspects of the system 100 to collectspectral CT projection data or information. For example, the X-raysource 712 may be alternated between high and low energies duringrotation of a gantry to acquire dual-energy CT information.

The memory 728 may include one or more computer readable storage media.The memory 728, for example, may store acquired CT information, valuesof parameters to be used in performing various aspects of the processflows or methods discussed herein, image data corresponding to imagesgenerated, results of intermediate processing steps, or the like.Further, the process flows and/or flowcharts discussed herein (oraspects thereof) may represent one or more sets of instructions that arestored in the memory 728 for direction operations of the system 700.

The output unit 740 is configured to provide information to the user.The output unit 740 may be configured to display, for example, one ormore material images, de-noised material images, or de-noised syntheticmonochromatic images, among others. The output unit 740 may include oneor more of a screen, a touchscreen, a printer, or the like.

The input unit 750 may be configured to obtain an input that correspondsto one or more settings or characteristics of a scan to be performed,and to provide the input (or information corresponding to the input) tothe processing unit 720, which may use the input to determine, adjust,or select the one or more parameters to be used in acquiring orprocessing imaging data. The input may include, for example, a portionof the body to be scanned (e.g., head, body). As another example, theinput may include one or more parameter values to be used for guidedde-noising, and/or information from which one or more such parametervalues may be determined. The input unit 750 may be configured to accepta manual user input, such as via a touchscreen, keyboard, mouse, or thelike. Additionally or alternatively, the input unit 750 may receiveinformation from another aspect of the imaging system 700, anothersystem, or a remote computer, for example, via a port or otherconnectivity device. The input unit 750 may also be configured to obtainuser approval or denial of a proposed scanning setting.

Various methods and/or systems (and/or aspects thereof) described hereinmay be implemented using a medical imaging system. For example, FIG. 8is a block schematic diagram of an exemplary CT imaging system 900 thatmay be utilized to implement various embodiments discussed herein.Although the CT imaging system 900 is illustrated as a standaloneimaging system, it should be noted that the CT imaging system 900 mayform part of a multi-modality imaging system in some embodiments. Forexample, the multi-modality imaging system may include the CT imagingsystem 900 and a positron emission tomography (PET) imaging system, or asingle photon emission computed tomography (SPECT) imaging system. Itshould also be understood that other imaging systems capable ofperforming the functions described herein are contemplated as beingused.

The CT imaging system 900 includes a gantry 910 that has the X-raysource 912 that projects a beam of X-rays toward the detector array 914on the opposite side of the gantry 910. A source collimator 913 and abowtie filter module 915 are provided proximate the X-ray source 912.The detector array 914 includes a plurality of detector elements 916that are arranged in rows and channels that together sense the projectedX-rays that pass through a subject 917. The imaging system 900 alsoincludes a computer 918 that receives the projection data from thedetector array 914 and processes the projection data to reconstruct animage of the subject 917. The computer 918, for example, may include oneor more aspects of the processing unit 720, or be operably coupled toone or more aspects of the processing unit 720. In operation, operatorsupplied commands and parameters are used by the computer 918 to providecontrol signals and information to reposition a motorized table 922.More specifically, the motorized table 922 is utilized to move thesubject 917 into and out of the gantry 910. Particularly, the table 922moves at least a portion of the subject 917 through a gantry opening(not shown) that extends through the gantry 910. Further, the table 922may be used to move the subject 917 vertically within the bore of thegantry 910.

The depicted detector array 914 includes a plurality of detectorelements 916. Each detector element 916 produces an electrical signal,or output, that represents the intensity of an impinging X-ray beam andhence allows estimation of the attenuation of the beam as it passesthrough the subject 917. During a scan to acquire the X-ray projectiondata, the gantry 910 and the components mounted thereon rotate about acenter of rotation 940. FIG. 8 shows only a single row of detectorelements 916 (i.e., a detector row). However, the multislice detectorarray 914 includes a plurality of parallel detector rows of detectorelements 916 such that projection data corresponding to a plurality ofslices can be acquired simultaneously during a scan.

Rotation of the gantry 910 and the operation of the X-ray source 912 aregoverned by a control mechanism 942. The control mechanism 942 includesan X-ray controller 944 that provides power and timing signals to theX-ray source 912 and a gantry motor controller 946 that controls therotational speed and position of the gantry 910. A data acquisitionsystem (DAS) 948 in the control mechanism 942 samples analog data fromdetector elements 916 and converts the data to digital signals forsubsequent processing. An image reconstructor 950 receives the sampledand digitized X-ray data from the DAS 948 and performs high-speed imagereconstruction. The reconstructed images are input to the computer 918that stores the image in a storage device 952. The computer 918 may alsoreceive commands and scanning parameters from an operator via a console960 that has a keyboard. An associated visual display unit 962 allowsthe operator to observe the reconstructed image and other data fromcomputer. It may be noted that one or more of the computer 918,controllers, or the like may be incorporated as part of a processingunit such as the processing unit 720 discussed herein.

The operator supplied commands and parameters are used by the computer918 to provide control signals and information to the DAS 948, the X-raycontroller 944 and the gantry motor controller 946. In addition, thecomputer 918 operates a table motor controller 964 that controls themotorized table 922 to position the subject 917 in the gantry 910.Particularly, the table 922 moves at least a portion of the subject 917through the gantry opening.

In various embodiments, the computer 918 includes a device 970, forexample, a CD-ROM drive, DVD drive, magnetic optical disk (MOD) device,or any other digital device including a network connecting device suchas an Ethernet device for reading instructions and/or data from atangible non-transitory computer-readable medium 972, that excludessignals, such as a CD-ROM, a DVD or another digital source such as anetwork or the Internet, as well as yet to be developed digital means.In another embodiment, the computer 918 executes instructions stored infirmware (not shown). The computer 918 is programmed to performfunctions described herein, and as used herein, the term computer is notlimited to just those integrated circuits referred to in the art ascomputers, but broadly refers to computers, processors,microcontrollers, microcomputers, programmable logic controllers,application specific integrated circuits, and other programmablecircuits, and these terms are used interchangeably herein.

In the exemplary embodiment, the X-ray source 912 and the detector array914 are rotated with the gantry 910 within the imaging plane and aroundthe subject 917 to be imaged such that the angle at which an X-ray beam974 intersects the subject 917 constantly changes. A group of X-rayattenuation measurements, i.e., projection data, from the detector array914 at one gantry angle is referred to as a “view” or “projection.” A“scan” of the subject 917 comprises a set of views made at differentgantry angles, or view angles, during one or more revolutions of theX-ray source 912 and the detector array 914. In a CT scan, theprojection data is processed to reconstruct an image that corresponds toa three-dimensional volume taken of the subject 917. It may be notedthat, in some embodiments, an image may be reconstructed using less thana full revolution of data. For example, with a multi-source system,substantially less than a full rotation may be utilized. Thus, in someembodiments, a scan (or slab) corresponding to a 360 degree view may beobtained using less than a complete revolution.

It should be noted that the various embodiments may be implemented inhardware, software or a combination thereof. The various embodimentsand/or components, for example, the modules, or components andcontrollers therein, also may be implemented as part of one or morecomputers or processors. The computer or processor may include acomputing device, an input device, a display unit and an interface, forexample, for accessing the Internet. The computer or processor mayinclude a microprocessor. The microprocessor may be connected to acommunication bus. The computer or processor may also include a memory.The memory may include Random Access Memory (RAM) and Read Only Memory(ROM). The computer or processor further may include a storage device,which may be a hard disk drive or a removable storage drive such as asolid-state drive, optical disk drive, and the like. The storage devicemay also be other similar means for loading computer programs or otherinstructions into the computer or processor.

As used herein, the term “computer” or “module” may include anyprocessor-based or microprocessor-based system including systems usingmicrocontrollers, reduced instruction set computers (RISC), ASICs, logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are exemplary only, andare thus not intended to limit in any way the definition and/or meaningof the term “computer”.

The computer or processor executes a set of instructions that are storedin one or more storage elements, in order to process input data. Thestorage elements may also store data or other information as desired orneeded. The storage element may be in the form of an information sourceor a physical memory element within a processing machine.

The set of instructions may include various commands that instruct thecomputer or processor as a processing machine to perform specificoperations such as the methods and processes of the various embodiments.The set of instructions may be in the form of a software program. Thesoftware may be in various forms such as system software or applicationsoftware and which may be embodied as a tangible and non-transitorycomputer readable medium. Further, the software may be in the form of acollection of separate programs or modules, a program module within alarger program or a portion of a program module. The software also mayinclude modular programming in the form of object-oriented programming.The processing of input data by the processing machine may be inresponse to operator commands, or in response to results of previousprocessing, or in response to a request made by another processingmachine.

As used herein, a structure, limitation, or element that is “configuredto” perform a task or operation is particularly structurally formed,constructed, or adapted in a manner corresponding to the task oroperation. For purposes of clarity and the avoidance of doubt, an objectthat is merely capable of being modified to perform the task oroperation is not “configured to” perform the task or operation as usedherein. Instead, the use of “configured to” as used herein denotesstructural adaptations or characteristics, and denotes structuralrequirements of any structure, limitation, or element that is describedas being “configured to” perform the task or operation. For example, aprocessing unit, processor, or computer that is “configured to” performa task or operation may be understood as being particularly structuredto perform the task or operation (e.g., having one or more programs orinstructions stored thereon or used in conjunction therewith tailored orintended to perform the task or operation, and/or having an arrangementof processing circuitry tailored or intended to perform the task oroperation). For the purposes of clarity and the avoidance of doubt, ageneral purpose computer (which may become “configured to” perform thetask or operation if appropriately programmed) is not “configured to”perform a task or operation unless or until specifically programmed orstructurally modified to perform the task or operation.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by acomputer, including RAM memory, ROM memory, EPROM memory, EEPROM memory,and non-volatile RAM (NVRAM) memory. The above memory types areexemplary only, and are thus not limiting as to the types of memoryusable for storage of a computer program.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the variousembodiments without departing from their scope. While the dimensions andtypes of materials described herein are intended to define theparameters of the various embodiments, they are by no means limiting andare merely exemplary. Many other embodiments will be apparent to thoseof skill in the art upon reviewing the above description. The scope ofthe various embodiments should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein.” Moreover, in thefollowing claims, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements on their objects. Further, the limitations of the followingclaims are not written in means-plus-function format and are notintended to be interpreted based on 35 U.S.C. §112, sixth paragraphunless and until such claim limitations expressly use the phrase “meansfor” followed by a statement of function void of further structure.

This written description uses examples to disclose the variousembodiments, including the best mode, and also to enable any personskilled in the art to practice the various embodiments, including makingand using any devices or systems and performing any incorporatedmethods. The patentable scope of the various embodiments is defined bythe claims, and may include other examples that occur to those skilledin the art. Such other examples are intended to be within the scope ofthe claims if the examples have structural elements that do not differfrom the literal language of the claims, or the examples includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

What is claimed is:
 1. A method comprising: obtaining spectral computedtomography (CT) information via an acquisition unit comprising an X-raysource and a CT detector; generating, with one or more processing units,using at least one image transform, a first basis image and a secondbasis image using the spectral CT information; performing, with the oneor more processing units, guided processing on the second basis imageusing the first basis image as a guide to provide a modified secondbasis image; performing a first inverse image transform using the firstbasis image and the modified second basis image to provide a firstmodified image; and performing a second inverse image transform usingthe first basis image and the modified second basis image to provide asecond modified image.
 2. The method of claim 1, wherein the spectral CTinformation includes dual energy CT information including first energyprojection data and second energy projection data corresponding to firstand second energies, respectively, and wherein generating the firstbasis image and the second basis image using at least one imagetransform comprises: performing a first material decomposition toprovide a first material image using the first energy projection dataand the second energy projection data; performing a second materialdecomposition to provide a second material image using the first energyprojection data and the second energy projection data; and performing afirst image transform using the first material image and the secondmaterial image to provide the first basis image; and performing a secondimage transform using the first material image and the second materialimage to provide the second basis image.
 3. The method of claim 2,wherein the first image transform is a linear transform configured toprovide a monochromatic image at an optimal keV level, wherein theoptimal keV level corresponds to an energy value at which a noise levelfor the first linear transform is minimized relative to other energyvalues.
 4. The method of claim 3, wherein the second image transform isa transpose of the first linear transform.
 5. The method of claim 4,wherein the first linear transform corresponds to an addition of thefirst material image and the second material image, and wherein thesecond linear transform corresponds to a subtraction of the firstmaterial image and the second material image.
 6. The method of claim 1,further comprising performing additional processing on the first basisimage to provide a modified first basis image, and wherein performingthe guided processing comprises using the modified first basis image. 7.The method of claim 1, wherein performing the guided processingcomprises filtering the second basis image with a filter having aweighting that varies as a function of information from the first basisimage.
 8. The method of claim 7, wherein the weighting is furtherconfigured to vary as a function of information from the second basisimage.
 9. The method of claim 7, wherein the weighting is configured asa tri-lateral filter having a first component, a second component, and athird component, wherein the first component varies as the function ofthe information from the first basis image, the second component variesas a function of information from the second basis image, and the thirdcomponent varies as a function of a distance between pixels.
 10. Themethod of claim 1, wherein the first basis image has a first noise leveland the second basis image has a second noise level that is higher thanthe first noise level.
 11. The method of claim 10, wherein performingthe guided processing comprises performing a guided de-noising on thesecond basis image using the first basis image.
 12. An imaging systemcomprising: a computed tomography (CT) acquisition unit comprising anX-ray source and a CT detector configured to collect spectral CTinformation of an object to be imaged, the X-ray source and CT detectorconfigured to be rotated about the object to be imaged and to collect aseries of projections of the object at plural energy levels as the X-raysource and CT detector rotate about the object to be imaged; and aprocessing unit comprising at least one processor operably coupled tothe CT acquisition unit, the processing unit configured to: control theCT acquisition unit to collect the spectral CT information of the objectto be imaged, generate, using at least one image transform, a firstbasis image and a second basis image using the spectral CT information;perform guided processing on the second basis image using the firstbasis image as a guide to provide a modified second basis image; andperforming at least one inverse transform using the first basis imageand the modified second basis image to generate at least one modifiedimage.
 13. The imaging system of claim 12, wherein the spectral CTinformation includes dual energy CT information including first energyprojection data and second energy projection data corresponding to firstand second energies, respectively, and wherein the processing unit isfurther configured to: perform a first material decomposition to providea first material image using the first energy projection data and thesecond energy projection data; perform a second material decompositionto provide a second material image using the first energy projectiondata and the second energy projection data; perform a first imagetransform using the first material image and the second material imageto provide the first basis image; and perform a second image transformusing the first material image and the second material image to providethe second basis image.
 14. The imaging system of claim 13, wherein thefirst image transform is a linear transform configured to provide amonochromatic image at an optimal keV level, wherein the optimal keVlevel corresponds to an energy value at which a noise level for thefirst linear transform is minimized relative to other energy values. 15.The imaging system of claim 12, wherein the processing unit isconfigured to perform the guided processing by filtering the secondbasis image with a filter having a weighting that varies as a functionof information from the first basis image.
 16. The imaging system ofclaim 15, wherein the weighting is configured as a tri-lateral filterhaving a first component, a second component, and a third component,wherein the first component varies as the function of the informationfrom the first basis image, the second component varies as a function ofinformation from the second basis image, and the third component variesas a function of a distance between pixels.
 17. A tangible andnon-transitory computer readable medium comprising one or more computersoftware modules configured to direct one or more processors to: obtainspectral computed tomography (CT) information via an acquisition unitcomprising an X-ray source and a CT detector; generate, using at leastone image transform, a first basis image and a second basis image usingthe spectral CT information; perform guided processing on the secondbasis image using the first basis image as a guide to provide a modifiedsecond basis image; and perform at least one inverse transform using thefirst basis image and the modified second basis image to generate atleast one modified image.
 18. The tangible and non-transitory computerreadable medium of claim 17, wherein the spectral CT informationincludes dual energy CT information including first energy projectiondata and second energy projection data corresponding to first and secondenergies, respectively, and wherein the computer readable medium isfurther configured to direct the one or more processors to: perform afirst material decomposition to provide a first material image using thefirst energy projection data and the second energy projection data;perform a second material decomposition to provide a second materialimage using the first energy projection data and the second energyprojection data; perform a first image transform using the firstmaterial image and the second material image to provide the first basisimage; and perform a second image transform using the first materialimage and the second material image to provide the second basis image.19. The tangible and non-transitory computer readable medium of claim17, wherein the computer readable medium is further configured to directthe one or more processors to filter the second basis image with afilter having a weighting that varies as a function of information fromthe first basis image to perform the guided processing.
 20. The tangibleand non-transitory computer readable medium of claim 19, wherein theweighting is configured as a tri-lateral filter having a firstcomponent, a second component, and a third component, wherein the firstcomponent varies as the function of the information from the first basisimage, the second component varies as a function of information from thesecond basis image, and the third component varies as a function of adistance between pixels.